System Identification Based Methods for the Development of Nonlinear Dynamic Models for Flight Simulators

基于系统辨识的飞行模拟器非线性动态模型开发方法

• 批准号: 543923-2019
• 负责人: Grant, Peter
• 金额: $ 3.79万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699742
• 关键词: System; Identification; Based; Methods; Development

Fixed wing and rotary wing training simulators are an important part of improving the safety of both civil and military aviation, and CAE is the world's largest supplier of training simulators. Arguably the most important part of a training simulator is the flight model as it drives all the simulator sub-systems. Therefore, the regulating bodies for civil aviation require the flight model behaviour to match that of the real aircraft within a specified set of tolerances. Military simulators have similar requirements. Flight models are therefore often developed by selecting the parameters of the model such that its behaviour matches recorded flight test data from the real aircraft. Outfitting the test aircraft with a vast array of instrumentation and flying a large set of test maneuvers is extremely time consuming and expensive. In addition, tuning the parameters of the flight model such that its behaviour matches the real aircraft, to within the required tolerances, is also extremely time-consuming process that requires significant involvement of expensive subject matter experts. This project aims to develop and apply new advanced system identification algorithms that will automatically estimate the parameters in a flight model such that its behaviour matches the recorded flight test data in an optimal sense. In addition, the same system identification algorithms will be used to determine the minimum airdata instrumentation requirements for the flight test aircraft that will result in a flight model with sufficient fidelity to comply with the appropriate regulations. Both of these tasks will greatly reduce CAE costs for developing training simulators, and potentially increase the fidelity of the simulators.

固定翼和旋翼训练模拟器是提高民用和军用航空安全的重要组成部分，CAE是世界上最大的训练模拟器供应商。可以说，训练模拟器最重要的部分是飞行模型，因为它驱动所有的模拟器子系统。因此，民用航空的管理机构要求飞行模型行为在特定的公差组内匹配真实的飞行器的行为。军事模拟器也有类似的要求。因此，飞行模型通常通过选择模型的参数来开发，使得其行为与来自真实的飞机的记录的飞行试验数据相匹配。为测试飞机配备大量的仪器和进行大量的测试机动是非常耗时和昂贵的。此外，调整飞行模型的参数以使其行为与真实的飞行器匹配到所需的公差内，也是极其耗时的过程，其需要昂贵的主题专家的大量参与。 该项目旨在开发和应用新的先进系统识别算法，自动估计飞行模型中的参数，使其行为与记录的飞行试验数据在最佳意义上相匹配。 此外，将使用相同的系统识别算法来确定飞行试验飞机的最低大气数据仪表要求，这将导致飞行模型具有足够的保真度，以符合适当的法规。这两项任务将大大降低开发培训模拟器的CAE成本，并可能提高模拟器的保真度。